1. KEY ROLE SPECIFIC ACCOUNTABILITIES 

Description of the Tasks

Performance Indicators (How is it measured)

Work closely with the senior data scientist, head of data science, and program managers to identify analytics opportunities based on a deep understanding of Client businesses and their information assets.

- Advanced analytics use cases successfully deployed

Implement models by transforming/optimizing for performance code and deploying them into production

- Productionised advanced analytics models

Support in the development of analytics standards and protocols to ensure scalability and support self-service advanced analytics

• Implemented standards and protocols
• Growth in citizen data scientist community

Research and stay up to date on latest data science and analytics approaches and practices in the market ensuring that, when relevant, these are appropriately leveraged within Data and Analytics function

- Up-to-date modern approaches and practices on data science

6. PERSON SPECIFIC:

Education: Master’s degree in Statistics, Machine Learning, Mathematics, Economics, Computer Science, Engineering, or any other related quantitative field.

Professional Skill Requirements

• You have a strength in the “design and prototype” part of the ML development pipeline, beginning with pulling datasets from SQL and ending with serializing ML models and assisting engineers to productionize model retraining and model serving systems
• You have practical experience of running impactful inferential analyses and data investigations to identify recurring patterns, root causes, and propose actionable product solutions
• You know how to design approaches to validate findings or test hypotheses, interpret experiments to measure the impact of new features
• You have excellent organizational, multi-tasking, and time-management skills

Minimum Experience and Knowledge:

• 3+ years of working experience in data science, with at least 1 years working in developing and deployment ML/DL solutions focused on batch and real-time data pipelines
• Experience with probability and statistics inclusive of machine learning, optimization, forecasting and experimental design
• Good understanding of machine learning & deep learning concepts and algorithms, various statistical techniques, and experimentation analysis workflows
• Experience in benchmarking and model performance evaluation and improvement

Job-Specific/Technical Skills required to complete the tasks:

• Proficient and experienced in scripting languages and have rapid prototyping skills, that is, fluency in SQL , Python , Spark, etc. among others
• Experience on the Azure Cloud platform: including Azure Databricks, Azure ML and etc.
• Experience working with Developer tools such as Azure DevOps, PyCharm/MS Visual Studio, Docker etc.